LSSA-BP-based cost forecasting for onshore wind power
An LSSA-BP neural network prediction model was established for more accurate onshore wind power cost prediction. Optimise the weights and thresholds of the BP neural network using the sparrow search algorithm. Comparison of the traditional BP model, GA-BP model and LSSA-BP model to verify the superi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722025847 |
| _version_ | 1797781944029675520 |
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| author | Ren Feng Liu Wencheng |
| author_facet | Ren Feng Liu Wencheng |
| author_sort | Ren Feng |
| collection | DOAJ |
| description | An LSSA-BP neural network prediction model was established for more accurate onshore wind power cost prediction. Optimise the weights and thresholds of the BP neural network using the sparrow search algorithm. Comparison of the traditional BP model, GA-BP model and LSSA-BP model to verify the superiority of the LSSA-optimised BP model. Moreover, using LSSA-BP in compared with Support Vector Regression Forecasting (SVR) and Random Forest Regression Forecasting (RFR) models. The results of model trial calculations and analysis showed that the LSSA-BP model had the highest prediction accuracy and could be used as a reference for the onshore wind power cost prediction. |
| first_indexed | 2024-03-13T00:04:06Z |
| format | Article |
| id | doaj.art-a2c765706a0b4333b8ebde78adb1ce37 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-13T00:04:06Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-a2c765706a0b4333b8ebde78adb1ce372023-07-13T05:28:38ZengElsevierEnergy Reports2352-48472023-12-019362370LSSA-BP-based cost forecasting for onshore wind powerRen Feng0Liu Wencheng1Department of Economic Management, North China Electric Power University, Baoding, 071003, Hebei, ChinaCorresponding author.; Department of Economic Management, North China Electric Power University, Baoding, 071003, Hebei, ChinaAn LSSA-BP neural network prediction model was established for more accurate onshore wind power cost prediction. Optimise the weights and thresholds of the BP neural network using the sparrow search algorithm. Comparison of the traditional BP model, GA-BP model and LSSA-BP model to verify the superiority of the LSSA-optimised BP model. Moreover, using LSSA-BP in compared with Support Vector Regression Forecasting (SVR) and Random Forest Regression Forecasting (RFR) models. The results of model trial calculations and analysis showed that the LSSA-BP model had the highest prediction accuracy and could be used as a reference for the onshore wind power cost prediction.http://www.sciencedirect.com/science/article/pii/S2352484722025847Wind power projectCost predictionSparrow search algorithmBP neural network model |
| spellingShingle | Ren Feng Liu Wencheng LSSA-BP-based cost forecasting for onshore wind power Energy Reports Wind power project Cost prediction Sparrow search algorithm BP neural network model |
| title | LSSA-BP-based cost forecasting for onshore wind power |
| title_full | LSSA-BP-based cost forecasting for onshore wind power |
| title_fullStr | LSSA-BP-based cost forecasting for onshore wind power |
| title_full_unstemmed | LSSA-BP-based cost forecasting for onshore wind power |
| title_short | LSSA-BP-based cost forecasting for onshore wind power |
| title_sort | lssa bp based cost forecasting for onshore wind power |
| topic | Wind power project Cost prediction Sparrow search algorithm BP neural network model |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722025847 |
| work_keys_str_mv | AT renfeng lssabpbasedcostforecastingforonshorewindpower AT liuwencheng lssabpbasedcostforecastingforonshorewindpower |